A comparative analysis of anti-vax discourse on twitter before and after COVID-19 onset

被引:5
作者
Nasralah, Tareq [1 ]
Elnoshokaty, Ahmed [2 ]
El-Gayar, Omar [3 ]
Al-Ramahi, Mohammad [4 ]
Wahbeh, Abdullah [5 ,6 ]
机构
[1] Northeastern Univ, DAmore McKim Sch Business, Supply Chain & Informat Management Grp, Boston, MA USA
[2] Northern Michigan Univ, Marquette, MI USA
[3] Dakota State Univ, Madison, SD USA
[4] Texas A&M Univ, San Antonio, TX USA
[5] Slippery Rock Univ Penn, Slippery Rock, PA USA
[6] Slippery Rock Univ, ATS 256,1 Morrow Way, Slippery Rock, PA 16057 USA
关键词
analytics; anti-vaxxers; COVID-19; social media; vaccines; VACCINE HESITANCY; SOCIAL MEDIA;
D O I
10.1177/14604582221135831
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
This study aimed to identify and assess the prevalence of vaccine-hesitancy-related topics on Twitter in the periods before and after the Coronavirus Disease 2019 (COVID-19) outbreak. Using a search query, 272,780 tweets associated with anti-vaccine topics and posted between 1 January 2011, and 15 January 2021, were collected. The tweets were classified into a list of 11 topics and analyzed for trends during the periods before and after the onset of COVID-19. Since the beginning of COVID-19, the percentage of anti-vaccine tweets has increased for two topics, "government and politics" and "conspiracy theories," and decreased for "developmental disabilities." Compared to tweets regarding flu and measles, mumps, and rubella vaccines, those concerning COVID-19 vaccines showed larger percentages for the topics of conspiracy theories and alternative treatments, and a lower percentage for developmental disabilities. The results support existing anti-vaccine literature and the assertion that anti-vaccine sentiments are an important public-health issue.
引用
收藏
页数:17
相关论文
共 59 条
[1]   COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data [J].
Ahmed, Wasim ;
Vidal-Alaball, Josep ;
Downing, Joseph ;
Lopez Segui, Francesc .
JOURNAL OF MEDICAL INTERNET RESEARCH, 2020, 22 (05)
[2]  
AJMC A., 2021, AM J MANAG CARE
[3]   Discovering design principles for health behavioral change support systems: A text mining approach [J].
Al-Ramahi, Mohammad A. ;
Liu, Jun ;
El-Gayar, Omar F. .
ACM Transactions on Management Information Systems, 2017, 8 (2-3)
[4]   Social media affordances and information abundance: Enabling fake news sharing during the COVID-19 health crisis [J].
Apuke, Oberiri Destiny ;
Omar, Bahiyah .
HEALTH INFORMATICS JOURNAL, 2021, 27 (03)
[5]  
Blankenship Elizabeth B, 2018, Perm J, V22, P17, DOI 10.7812/TPP/17-138
[6]   Latent Dirichlet allocation [J].
Blei, DM ;
Ng, AY ;
Jordan, MI .
JOURNAL OF MACHINE LEARNING RESEARCH, 2003, 3 (4-5) :993-1022
[7]  
Bonnevie Erika, 2021, Journal of Communication in Healthcare, V14, P12, DOI 10.1080/17538068.2020.1858222
[8]   The social life of COVID-19: Early insights from social media monitoring data collected in Poland [J].
Burzynska, Joanna ;
Bartosiewicz, Anna ;
Rekas, Magdalena .
HEALTH INFORMATICS JOURNAL, 2020, 26 (04) :3056-3065
[9]  
Butler Kiera, 2020, Mother Jones
[10]  
Cavnar W., 1994, P SDAIR 94 3 ANN S D, V3, P161